What do you think?
Rate this book
Mapping Forest Landscape Patterns
Preface
Chapter 1: Mapping forest overview and a primer 1. Mapping forest an introduction 1.1 What is mapping? 1.2 What is a forest landscape? 2. Considerations in forest landscape mapping 2.1 Describing spatial patterns2.2 Focus on boundaries2.3 Beyond 2D data 3. Utility of forest landscape maps 3.1 Map representations3.2 Morphological interpretations3.3 Map scale3.4 Error assessment and validation 4. Summary
Chapter 2: Fuzzy classification of vegetation for ecosystem mapping 1. Introduction 2. Overview of fuzzy systems 2.1 Fuzzy systems - key concepts for mapping2.2 Mapping with fuzzy classifiers 3. Fuzzy approaches for identifying and utilizing uncertainty 3.1 Thematic uncertainty3.2 Spatial uncertainty3.3 Simultaneous considerations of thematic and spatial uncertainty3.4 Multiple outputs - fuzzy geodatabase 4. Vertical structure mapping 5. A look to the future 6. Summary
Chapter 3: Portraying wildfires in forest landscapes as discrete complex objects 1. Introduction 2. Wildfire initiation and anatomy 2.1 Initiation2.2 Descriptors of footprints 3. Wildfires as discrete and complex objects 3.1 The outer edge of a wildfire is scale-dependent 3.2 Width of the ecotone3.3 Internal heterogeneity 4. Standardized depiction of wildfires as discrete complex objects 5. The future of mapping wildfires 5.1 Accuracy assessment in remote regions5.2 Landscape persistence5.3 Hierarchical data formats for capturing scale effects
Chapter 4: Airborne LiDAR applications in forest landscapes 1. Introduction 1.1 Defining ALS LiDAR 1.2 Introduction to the three common LiDAR platforms1.3 Intensity, point density, and multi-spectral LiDAR 2. Primary measurements 2.1 Surface models (DEM, DSM, DTM, CHM)2.2 Canopy height models and detection and delineation of individual trees 3. Secondary measurements 3.1 Regression models and allometric equations3.2 Vertical profile for a single tree3.3 Classification of vegetation types3.4 Tree genus and species classification3.5 Case identifying potentially hazardous trees 4. The future of LiDAR
Chapter 5: Regression Tree modeling of spatial pattern and process interactions 1. Spatial Pattern and Processes 1.1 Describing spatial patterns1.2 Process complexity1.3 Data mining 2. Methods 2.1 CART models2.2 BRT2.3 RF models 3. Case Study Context - Influence of beetle infestation spatial patterns on fire spatial processes 3.1 Study area3.2 Spatial data 4. Model evaluation 4.1 CART4.2 BRTs4.3 RF models4.4 Comparing modeling approaches 5. Interpreting regression tree results within the context of spatial pattern and process
Chapter 1: Mapping forest overview and a primer 1. Mapping forest an introduction 1.1 What is mapping? 1.2 What is a forest landscape? 2. Considerations in forest landscape mapping 2.1 Describing spatial patterns2.2 Focus on boundaries2.3 Beyond 2D data 3. Utility of forest landscape maps 3.1 Map representations3.2 Morphological interpretations3.3 Map scale3.4 Error assessment and validation 4. Summary
Chapter 2: Fuzzy classification of vegetation for ecosystem mapping 1. Introduction 2. Overview of fuzzy systems 2.1 Fuzzy systems - key concepts for mapping2.2 Mapping with fuzzy classifiers 3. Fuzzy approaches for identifying and utilizing uncertainty 3.1 Thematic uncertainty3.2 Spatial uncertainty3.3 Simultaneous considerations of thematic and spatial uncertainty3.4 Multiple outputs - fuzzy geodatabase 4. Vertical structure mapping 5. A look to the future 6. Summary
Chapter 3: Portraying wildfires in forest landscapes as discrete complex objects 1. Introduction 2. Wildfire initiation and anatomy 2.1 Initiation2.2 Descriptors of footprints 3. Wildfires as discrete and complex objects 3.1 The outer edge of a wildfire is scale-dependent 3.2 Width of the ecotone3.3 Internal heterogeneity 4. Standardized depiction of wildfires as discrete complex objects 5. The future of mapping wildfires 5.1 Accuracy assessment in remote regions5.2 Landscape persistence5.3 Hierarchical data formats for capturing scale effects
Chapter 4: Airborne LiDAR applications in forest landscapes 1. Introduction 1.1 Defining ALS LiDAR 1.2 Introduction to the three common LiDAR platforms1.3 Intensity, point density, and multi-spectral LiDAR 2. Primary measurements 2.1 Surface models (DEM, DSM, DTM, CHM)2.2 Canopy height models and detection and delineation of individual trees 3. Secondary measurements 3.1 Regression models and allometric equations3.2 Vertical profile for a single tree3.3 Classification of vegetation types3.4 Tree genus and species classification3.5 Case identifying potentially hazardous trees 4. The future of LiDAR
Chapter 5: Regression Tree modeling of spatial pattern and process interactions 1. Spatial Pattern and Processes 1.1 Describing spatial patterns1.2 Process complexity1.3 Data mining 2. Methods 2.1 CART models2.2 BRT2.3 RF models 3. Case Study Context - Influence of beetle infestation spatial patterns on fire spatial processes 3.1 Study area3.2 Spatial data 4. Model evaluation 4.1 CART4.2 BRTs4.3 RF models4.4 Comparing modeling approaches 5. Interpreting regression tree results within the context of spatial pattern and process
342 pages, Paperback
Published September 13, 2017
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
No one has reviewed this book yet.